Preliminary validation of two temporal parameter-based soil moisture retrieval models using a satellite product and in situ soil moisture measurements over the REMEDHUS network

This study aims to preliminarily validate two newly developed temporal parameter-based surface soil moisture (SSM) retrieval models, namely the mid-morning model and daytime model, using both microwave satellite soil moisture product and in situ SSM measurements over a well-organized soil moisture network named REd de MEDición de la HUmedad del Suelo (REMEDHUS) in Spain. Ground SSM measurements and geostationary satellite observations were primarily implemented to obtain the model coefficients for the two SSM retrieval models for each cloud-free day. These model coefficients were subsequently used to estimate SSM using the Meteosat Second Generation products over the study area. Preliminary verification using both a satellite product and in situ SSM measurements demonstrated that SSM variation can be well detected by both SSM retrieval models. Specifically, a generally similar accuracy (coefficient of determination R²: 0.419–0.379, root mean square error: 0.046–0.051 m³ m^?3, Bias: ?0.020 to ?0.025 m³ m^?3) was found for the mid-morning model and the daytime model with the microwave missions based climate change initiative SSM product, respectively. Moreover, except for the comparable R² (0.614–0.675), a better accuracy (Bias: 0.032–0.044 m³ m^?3, RMSE: 0.043–0.050 m³ m^?3) are achieved for the daytime model and the mid-morning model with network SSM measurements, respectively. These results indicate that the daytime model exhibited generally comparable or better accuracy than that of the mid-morning model over the study area. This study has strengthened the feasibility of using multi-temporal information derived from the geostationary satellites to estimate SSM in future research.     

An appraisal of the accuracy of operational soil moisture estimates from SMOS MIRAS using validated in situ observations acquired in a Mediterranean environment

Acquiring information on the spatio-temporal variability of soil moisture is of key importance in extending our capability to understand the Earth system’s physical processes, and is also required in many practical applications. Earth observation (EO) provides a promising avenue to observe the distribution of soil moisture at different observational scales, with a number of products distributed at present operationally. Validation of such products at a range of climate and environmental conditions across continents is a fundamental step related to their practical use. Various in situ soil moisture ground observational networks have been established globally providing suitable data for evaluating the accuracy of EO-based soil moisture products. This study aimed at evaluating the accuracy of soil moisture estimates provided from the Soil Moisture and Ocean Salinity Mission (SMOS) global operational product at test sites from the REMEDHUS International Soil Moisture Network (ISMN) in Spain. For this purpose, validated observations from in situ ground observations acquired nearly concurrent to SMOS overpass were utilized. Overall, results showed a generally reasonable agreement between the SMOS product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer (root mean square error (RMSE) = 0.116 m³ m^?3). An improvement in product accuracy for the overall comparison was shown when days of high radio frequency interference were filtered out (RMSE = 0.110 m³ m^?3). Seasonal analysis showed highest agreement during autumn, followed by summer, winter, and spring seasons. A systematic soil moisture underestimation was also found for the overall comparison and during the four seasons. Overall, the result provides supportive evidence of the potential value of this operational product for meso-scale studies and practical applications.     

Detecting changes in surface soil moisture content using differential SAR interferometry

The feasibility of measuring changes in surface soil moisture content with differential interferometric synthetic aperture radar (DInSAR) has received little attention in comparison with other active microwave techniques. In this study, multi-polarization C- and L-band DInSAR is explored as a potential tool for the measurement of changes in surface soil moisture in agricultural areas. Using 10 ascending phased array L-band SAR (PALSAR) scenes acquired by the Japanese Advanced Land Observing Satellite (ALOS) and 12 descending advanced SAR (ASAR) scenes acquired by the European ENVISAT satellite between July 2007 and November 2009, a series of 27 differential interferograms covering a common study area over southern Ireland were generated to investigate whether small-scale changes in phase are linked to measured soil moisture changes. Comparisons of observed mean surface displacement and in situ mean soil moisture change show that C-band cross-polarization pairs displayed the highest correlation coefficients over both the barley (correlation coefficient, r = 0.51, p = 0.04)- and potato crop (r = 0.81, p = 0.003)-covered fields. Current results support the hypothesis that a soil moisture phase contribution exists within differential interferograms covering agricultural areas.     

A re‐examination of perpendicular drought indices

In this letter, the performance of newly developed drought indices, the perpendicular drought index (PDI) and modified perpendicular drought index (MPDI), are further explored for regional surface dryness monitoring to provide clear guidance on appropriate implementation of these indices over different eco‐systems through in‐depth analysis of their advantages and constraints. Spatio‐temporal patterns of surface drought derived by MODerate Resolution Imaging Spectroradiometer (MODIS)‐based PDI and MPDI are compared against field‐measured soil moisture (SM), rainfall, and regional hydrological conditions. Results indicate that there are significant negative correlations between the PDI, the MPDI, and mean 0–20 cm SM content and rainfall. The PDI and the MPDI provide similar results at the early stage of vegetation growth, but a greater agreement between the drought information extracted by the MPDI and field measurements is observed for vegetated surfaces where the PDI fails. Therefore, it is recommended that PDI be used for bare soil applications, since it does not require calculation of additional information such as the fraction of vegetation which may contain some uncertainties, but the MPDI should be used for vegetated regions.     

Relationship between satellite microwave radiometric data,antecedent precipitation index,and regional soil moisture

Abstract

Satellite microwave brightness temperatures (T_B 'S) have been shown, in previous studies for semi-arid environments, to correlate well with the antecedent precipitation index (API), a soil moisture indicator. The current study, using the Special Sensor Microwave/Imager (SSM/I), continued this work for parts of the U.S. Corn and Wheat Belts, which included areas with a more humid climate, a denser natural vegetation cover, and a different mix of agricultural crop types. Four years (1987-1990) of SSM/I data at 19 and 37GHz, daily precipitation and temperature data from weather stations, and API calculated from the weather data were processed, geo-referenced, and averaged to equation pending latitude-longitude grid quadrants. Correlation results between T_B at 19 GHz and API were highly dependent on geographical location. Correlation coefficients (r values) ranged from —0-6 to —0-85 for the semi-arid parts of the study area and from —03 to —0-7 for the more humid and more densely vegetated parts. R values were also higher for the very dry and very wet years (—0-5 to —085) than for the 'normal’ year (—0-3 to —0-65). Similar to previous results, the Microwave Polarization Difference Index (MPDI), based on the 37 GHz data, was found to correspond to variations in vegetation cover. The MPDI was used to develop a linear regression model to estimate API from T_B . Correlation between estimated and calculated APIs was also geographically and time dependent. Comparison of API with some field soil moisture measurements showed a similar trend, which provided some degree of confidence in using API as an indicator of soil moisture.     

The effect of soil moisture on the 37GHz microwave polarization difference index (MPDI)

Previous studies have shown that the 37 GHz microwave polarization difference index (MPDI) has an inverse nonlinear relationship to the normalized difference vegetation index (NDVI) with the MPDI (NDVI) being more sensitive to vegetation density under sparse (moderate) vegetation conditions. It has also been noted that soil moisture can have a significant influence on the MPDI. This study quantifies the effect of soil moisture on the MPDI using the RADTRAN model and comparison with measurements from a few geographically restricted (eastern USA) study sites. Model results show the MPDI increases with soil moisture but its sensitivity approaches zero when soil moisture values or vegetation densities are large. Results based on special sensor microwave/imager (SSM/I) measured values of MPDI, using the NDVI as a surrogate for vegetation density and an antecedent precipitation index (API) as a surrogate for soil moisture, were consistent with those based on the model. Linear equations, one for each of three categories of vegetation density, expressing MPDI as a function of API were derived based on SSM/I measurements. These equations demonstrate that soil moisture information can be extracted from the MPDI when the NDVI is used to account for the effect of vegetation and that the effect of soil moisture on the MPDI should be taken into account if it is to be used as a vegetation index. The potential to normalize MPDI values for variations in soil moisture is discussed.     

An algorithm to estimate soil moisture over vegetated areas based on in situ and remote sensing information

An algorithm is proposed for estimating soil moisture over vegetated areas. The algorithm uses in situ and remote sensing information and statistical tools to estimate soil moisture at 1 km spatial resolution and at 20 cm depth over Puerto Rico. Soil moisture within the study region is characterized by spatial and temporal variability. The temporal variability for a given area exhibits long- and short-term variations that can be expressed by two empirical models. The average monthly soil moisture exhibits the long-term variability and is modelled by an artificial neural network (ANN), whereas the short-term variability is determined by hourly variation and is represented by a nonlinear stochastic transfer function model. Monthly vegetation index, land surface temperature, accumulated rainfall and soil texture are the major drivers of the ANN to estimate the monthly soil moisture. Radar, satellite and in situ observations are the major sources of information of the soil moisture empirical models. A self-organized ANN was also used to identify spatial variability to be able to determine a similar transfer function that best resembles the properties of a particular grid point and estimate the hourly soil moisture across the island. Validation techniques reveal an average absolute error of 3.34% of volumetric water content and this result shows that the proposed algorithm is a potential tool for estimating soil moisture over vegetated areas.     

Intercomparison of microwave remote-sensing soil moisture data sets based on distributed eco-hydrological model simulation and in situ measurements over the North China Plain

Intercomparisons of microwave-based soil moisture products from active ASCAT (Advanced Scatterometer) and passive AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System) is conducted based on surface soil moisture (SSM) simulations from the eco-hydrological model, Vegetation Interface Processes (VIP), after it is carefully validated with in situ measurements over the North China Plain. Correlations with VIP SSM simulation are generally satisfactory with average values of 0.71 for ASCAT and 0.47 for AMSR-E during 2007–2009. ASCAT and AMSR-E present unbiased errors of 0.044 and 0.053 m³ m^?3 on average, with respect to model simulation. The empirical orthogonal functions (EOF) analysis results illustrate that AMSR-E provides more consistent SSM spatial structure with VIP than ASCAT; while ASCAT is more capable of capturing SSM temporal dynamics. This is supported by the facts that ASCAT has more consistent expansion coefficients corresponding to primary EOF mode with VIP (R = 0.825, p < 0.1). However, comparison based on SSM anomaly demonstrates that AMSR-E and ASCAT have similar skill in capturing SSM short-term variability. Temporal analysis of SSM anomaly time series shows that AMSR-E provides best performance in autumn, while ASCAT provides lower anomaly bias during highly-vegetated summer with vegetation optical depth of 0.61. Moreover, ASCAT retrieval accuracy is less influenced by vegetation cover, as it is in relatively better agreement with VIP simulation in forest than in other land-use types and exhibits smaller interannual fluctuation than AMSR-E. Identification of the error characteristics of these two microwave soil moisture data sets will be helpful for correctly interpreting the data products and also facilitate optimal specification of the error matrix in data assimilation at a regional scale.     

Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach

An approach is evaluated for the estimation of soil moisture at high resolution using satellite microwave and optical/infrared (IR) data. This approach can be applied to data acquired by the Visible/Infrared Imager Radiometer Sensor Suite (VIIRS) and a Conical Scanning Microwave Imager/Sounder (CMIS), planned for launch in the 2009–2010 time frame under the National Polar-Orbiting Operational Environmental Satellite System (NPOESS). The approach for soil moisture estimation involves two steps. In the first step, a passive microwave remote sensing technique is employed to estimate soil moisture at low resolution (～25?km). This involves use of a simplified radiative transfer model to invert dual-polarized microwave brightness temperature. In the second step, the microwave-derived low-resolution soil moisture is linked to the scene optical/IR parameters, such as Normalized Difference Vegetation Index (NDVI), surface albedo, and Land Surface Temperature (LST). The linking is based on the ‘Universal Triangle’ approach of relating land surface parameters to soil moisture. The optical/IR parameters are available at high resolution (～1?km) but are aggregated to the microwave resolution for the purpose of building the linkage model. The linkage model in conjunction with high-resolution NDVI, surface albedo and LST is then used to disaggregate microwave soil moisture into high-resolution soil moisture. The technique is applied to data from the Special Sensor Microwave Imager (SSM/I) and Advanced Very High Resolution Radiometer (AVHRR) acquired for the Southern Great Plains (SGP-97) experiment conducted in Oklahoma in June–July 1997. An error budget analysis performed on the estimation procedure shows that the rms error in the estimation of soil moisture is of the order of 5%. Predicted soil moisture results at high resolution agree reasonably well with low resolution results in both magnitude and spatio-temporal patterns. The high resolution results are also compared with in situ (0–5?cm deep) point measurements. While the trends are similar, the soil moisture estimates in the two cases are different. Issues involving comparison of satellite derived soil moisture with in situ point measurements are also discussed.     

Monitoring surface water content using visible and short-wave infrared SPOT-5 data of wheat plots in irrigated semi-arid regions

Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high-resolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012–2013 and 2013–2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R² = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold cross-validation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas.     

Assessment of the AMSR-E soil moisture product over India

Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales, controlling the exchange of water and energy between the atmosphere and land surface. Satellite-based microwave radiometric observations are considered to be the best for soil moisture remote sensing because of their high sensitivity, as well as their all-weather and day–night observation capabilities with high repeativity. In this study, an attempt has been made to assess the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-EOS) soil moisture product over India. The AMSR-E soil moisture product has been assessed using in situ soil moisture observations made by the India Meteorological Department (IMD) during the monsoon period (May–August) for the years 2002–2006 over 18 meteorological stations. Apart from assessing AMSR-E soil moisture retrieval accuracy, this study also investigates the effect of vegetation, topography and coastal water contamination, and determines the regions where the AMSR-E soil moisture product could be useful for different applications.     

Validation of a practical normalized soil moisture model with in situ measurements in humid and semi-arid regions

Soil moisture is an important parameter that influences the exchange of water and energy fluxes between the land surface and the atmosphere. Through the simulation by a Soil–Vegetation–Atmosphere Transfer model, Carlson proposed the universal spatial information-based method to determine soil moisture that is insensitive to the initial atmospheric and surface conditions, net radiation, and atmospheric correction. In this study, a practical normalized soil moisture model is established to describe the relationship among the normalized soil moisture (M), the normalized land surface temperature (T*), and the fractional vegetation cover. The dry and wet points are determined using the surface energy balance principle, which has a robust physical basis. This method is applied to retrieve soil moisture for the Soil Moisture-Atmosphere Coupling Experiment campaign in the Walnut Creek watershed, which has a humid climate, and at the Linzestation, which has a semi-arid climate. The validation data are obtained on days of year (DOYs) 182 and 189 in 2002 in the humid region and on DOYs 148 and 180 in 2008 for the semi-arid region; these data collection days are coincident with the overpass of the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus. When the estimates are compared with the in situ measurements of soil water content, the root mean square error is approximately 0.10 m³ m^?3 with a bias of 0.05 m³ m^?3 for the humid region and 0.08 m³ m^?3 with a bias of 0.03 m³ m^?3 for the semi-arid region. These results demonstrate that the practical normalized soil moisture model is applicable in both humid and semi-arid regions.     

Relationships between vegetation indices and root zone soil moisture under maize and soybean canopies in the US Corn Belt: a comparative study using a close-range sensing approach

Understanding the relationships between root zone soil moisture and vegetation spectral signals will enhance our ability to manage water resources and monitor drought-related stress in vegetation. In this article, the relationships between vegetation indices (VIs) and in situ soil moisture under maize and soybean canopies were analysed using close-range reflectance data acquired at a rainfed cropland site in the US Corn Belt. Because of the deep rooting depths of maize plants, maize-based VIs exhibited significant correlations with soil moisture at a depth of 100 cm (P < 0.01) and kept soil moisture memory for a long period of time (45 days). Among the VIs applied to maize, the chrolophyll red-edge index (CI_red-edge) correlated best with the concurrent soil moisture at 100 cm depth (P < 0.01) for up to 20 day lag periods. The same index showed a significant correlation with soil moisture at a 50 cm depth for lag periods from 10 (P < 0.05) to 60 days (P < 0.01). VIs applied to soybean resulted in statistically significant correlations with soil moisture at the shallower 10 and 25 cm depths, and the correlation coefficients declined with increasing depths. As opposed to maize, soybean held a shorter soil moisture memory as the correlations for all VIs versus soil moisture at 10 cm depth were strongest for the 5 day lag period. Wide dynamic range VI and normalized difference VI performed better in characterizing soil moisture at the 10 and 25 cm depths under soybean canopies when compared with enhanced VI and CI_red-edge.     

Upscaling of sparse in situ soil moisture observations by integrating auxiliary information from remote sensing

Upscaling of sparse in situ soil moisture (SM) observations is essential for the validation of current and upcoming space-borne SM retrievals, and the successful application of SM observations in hydrological models or data assimilation. In this study, we construct a novel method based on Bayesian data fusion to upscale in situ SM observations to the coarse scale of microwave remote sensing. In the framework of Bayesian theory, the valuable auxiliary information obtained in Moderate Resolution Imaging Spectroradiometer (MODIS) apparent thermal inertia (ATI) is integrated into the upscaling process. The method is validated using SM wireless sensor network data in the Tibetan plateau, which covers an area of approximately 30 × 30 km² with 20 in situ stations. Results confirm that the upscaled SM using the method with randomly selected three stations from the 20 stations is extremely close to the mean of the 20 SMs. The mean root mean square error (RMSE) between the upscaled SM and the mean of the 20 in situ SMs was 0.02 m³ m^?3, and the max RMSE was less than 0.05 m³ m^?3. Furthermore, the sensitivity of the upscaling accuracy to the number of in situ observations is discussed. When the number of in situ observations is greater than nine, the increasing accuracy of the Bayesian method is limited by the uncertainty in the ATI of the remote sensing.     

Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China

In this paper, drought status of northwestern China is evaluated using the Terra–Moderate Resolution Imaging Spectroradiometer (MODIS) data with a newly developed method called perpendicular drought index (PDI), which is defined as a line segment that is parallel with the soil line and perpendicular to the normal line of soil line intersecting the coordinate origin in the two‐dimensional scatter plot of red against near infrared (NIR) wavelength reflectance. To validate the PDI in macroscale applications, quantitative evaluation of drought conditions in Ningxia, Northwestern China is carried out by comparing the PDI with one of the well‐known drought indexes, namely, temperature‐vegetation index (TVX). Linear regression between ground‐measured soil moisture data and the PDI and the TVX was made. Results show that satellite based PDI and TVX has significant correlation with 0–20 cm averaged soil moisture obtained over the meteorological observing stations across the whole study area. The highest correlation of R ² = 0.48 for the PDI and R ² = 0.40 for the TVX is obtained when compared with average soil moisture from 0 to 20 cm soil depth. According to the drought critical values defined by soil hydrologic parameters including soil moisture, wilting coefficient and field moisture capacity, the PDI based drought guidelines are established, and then the drought status in the study area is evaluated using the PDI. It is evident from the results showing the spatial distribution of drought in northwestern China that the PDI is highly accordant with field drought status.     

Estimating sub-pixel temperatures using the triangle algorithm

In water-deficient areas, water resource management requires evapotranspiration at high spatiotemporal resolution – an impossible situation given the trade-off between spatial and temporal resolutions in space-borne systems. Some researchers have suggested sharpening the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature product with a resolution from 1 km to 250 m and a functional relationship between surface temperature (T _r) and normalized difference vegetation index (NDVI). Evapotranspiration at 250 m resolution can be obtained once every few days using this technique. Based on the interpretation of the triangular T _r–NDVI space and assuming uniform soil moisture conditions in a coarse pixel, this paper suggests an alternative algorithm – the triangle algorithm – for sharpening. The triangle algorithm was tested using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data from an arid zone. Sharpened surface temperatures and reference temperatures were compared at 60 m and 240 m resolutions. Root mean square errors with the triangle algorithm are smaller than those with a functional relationship between T _r and NDVI. This paper will also discuss the impact of soil moisture variations in the coarse pixel on the triangle algorithm. Finally we should mention that the triangle algorithm only applies to regions with non-stressed vegetation canopies.     

Spatial and temporal patterns of surface soil moisture over India estimated using surface wetness index from SSM/I microwave radiometer

Results from an approach to infer surface soil moisture from time series analysis of surface wetness index derived using the Special Sensor Microwave/Imager (SSM/I) are presented. Soil moisture quantification was based on the study of temporal changes in surface wetness index and its scaling to maximum and air‐dry limits of soil in each grid cell (0.33°). The estimated soil moisture of Illinois, USA was compared with field measured soil moisture (0–10?cm) obtained from the Global Soil Moisture Data Bank. A root mean square error of 7.18% was found between estimated and measured volumetric soil moisture. A consistency in soil moisture and rainfall pattern was found in the un‐irrigated areas of northern India (Jodhpur, Varanasi) and southern India (Madurai), influenced by southwest and northeast monsoons, respectively. Soil moisture of more than 0.30 m³m^?3 was observed in the absence of rainfall due to the irrigation of rice crop in (Punjab) during the pre‐southwest monsoon period (May).     

The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

ABSTRACT

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter α_s. In addition to the parameter α_s from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR’2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9–12 and 9–15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters α_s and χ for ε_s > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season.     

被动微波遥感指数及其应用

分别概述了微波极化指数、散射指数以及土壤湿度指数等被动微波遥感指数的发展及其应用。37GHz的微波极化差指数△T37（△T37=TB37V—TB37H）和极化比指数（MPDI=C＊（TB37V—TB37H）／（TB37V＋TB37H））被认为是监测植被状况的微波植被指数，利用GAME—Tibet1998IOP数据计算和分析了青藏高原中部5个试验站点6～9月的平均△T37值和MPDI值的变化情况。结果表明：ANDUO和MS3608的平均值在15K左右，表现出裸土的微波辐射特征；总体上5个站点的MPDI随时间的变化不大，也即在1998年6～9月间，各个站点的植被状况变化不大；而站间的差别比较大，也即各个站点的植被状况有较大的差别；ANDUO的MPDI表现出规律性的变化，即在6至9月的变化中，8月份的MPDI最小，对应植被最好的月份；对研究区的MPDI和相应时间的MSAVI（可见／近红外数据得到的修改型土壤调整植被指数）的空间分布图进行了比较，二者基本吻合。